Pachinko Prediction: A Bayesian method for event prediction from social media data

TL;DR

This paper introduces a Bayesian approach that combines machine learning classification of social media posts with empirical Bayesian inference to predict social unrest events in Australia, accounting for uncertainty in diverse data sources.

Contribution

It presents a novel Bayesian framework integrating machine learning and empirical Bayesian methods for event prediction from social media data.

Findings

Effective prediction of social unrest events in Australian cities.

Quantification of uncertainty in event probability estimates.

Demonstration of the method's applicability to real-world social media data.

Abstract

The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, accounting for uncertainty in such models, particularly when using diverse, unstructured datasets such as social media, is essential to guarantee the appropriate use of such methods. Here we develop a Bayesian method for predicting social unrest events in Australia using social media data. This method uses machine learning methods to classify individual postings to social media as being relevant, and an empirical Bayesian approach to calculate posterior event probabilities. We use the method to predict events in Australian cities over a period in 2017/18.